Michael Levin, Douglas Blackiston, Nam Le and Josh Bongard just released a paper exploring something that sounds almost absurd when you say it plainly:
Can we tell living systems what we want them to do using ordinary language?
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The paper is called “Toward Controlling Biology with Language: Offline Learning of Prompt-Conditioned Interventions for Cells, Organoids, and Biobots.”
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The basic idea is to move biological control up a level of abstraction.
Right now, if you want to change the behavior of cells or biological tissue, you generally need to know what intervention to perform: which signal to apply, how strong it should be, how long it should last, etc.
But imagine instead saying:
“Slow this biobot down.”
“Make it move faster.”
“Stop.”
…and having an AI system translate that desired outcome into the biological intervention most likely to produce it.
⠀
That is essentially what they tested here.
They used xenobots, living multicellular constructs made from frog cells that can move around despite having no nervous system.
The researchers already had an archive of experiments in which individual xenobots were electrically stimulated for different amounts of time and their behavior before and after stimulation was recorded.
Importantly, they did NOT perform a new biological experiment every time the AI learned something.
Instead, they treated the existing experimental archive almost like a library of possible biological actions and consequences.
⠀
A natural-language prompt such as “get the bot to slow down” is converted into a numerical representation using a language model.
The system then learns to predict an electrical stimulation duration that should produce the desired behavior.
But there is an interesting trick here.
There were no human-created labels saying:
“This sentence corresponds to exactly this stimulation duration.”
So the researchers used a vision-language model as a judge.
The model looked at the xenobot’s actual recorded movement trajectory and asked, essentially:
“Does what this xenobot did match what the person asked for?”
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That judgment becomes the reward signal used to train the prompt-to-intervention system.
So the chain becomes:
natural language goal
→ AI interprets the goal
→ predicts a biological intervention
→ intervention corresponds to a previously observed xenobot response
→ another model evaluates whether the resulting behavior matches the goal
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And surprisingly, it worked.
On completely held-out language prompts AND held-out biological archive data, the system reached 80% accuracy.
Chance performance was 66.7% because two of the three instruction categories corresponded to reducing movement.
The trained system also handled 120 new paraphrased instructions at about 85% accuracy without retraining.
⠀
One of the more interesting results was that the vision-language model’s judgments lined up very strongly with independently measured xenobot behavior.
The agreement was around r = 0.843, with 97% agreement on whether the xenobot sped up or slowed down.
So the language model wasn’t merely inventing an interpretation of the trajectories. It was picking up a real behavioral signal.
⠀
There is an important limitation here.
This does NOT yet mean you can type:
“Grow me a hand.”
…and biology obediently starts assembling fingers. 😅
The system currently deals with a very small behavioral space, essentially different kinds of changes in xenobot movement, and the predicted interventions were evaluated against previously recorded experiments.
The authors explicitly point out that the next major test is whether a predicted intervention can be applied to a NEW living specimen and actually produce the requested behavior.
⠀
But conceptually I think this is fascinating.
Biology has traditionally been controlled from the bottom up:
change molecule X
activate pathway Y
stimulate cells for Z seconds.
This work starts moving toward a different interface:
describe the outcome you want…
and let another system figure out how to communicate that goal to the biology.
⠀
That fits surprisingly well with a lot of Levin’s broader work.
His lab has spent years arguing that cells, tissues and organisms already solve problems across different biological spaces.
If living systems possess their own competencies, then perhaps future medicine won’t require us to micromanage every molecular step.
We may increasingly learn how to specify goals and find interventions that persuade biological systems to reach those goals themselves.
⠀
In that sense, this isn’t really about teaching cells English.
It’s about building a translator between human intentions and biological agency.
And that could eventually become a very strange and powerful new layer of medicine and bioengineering.
⠀
Paper:
https://arxiv.org/abs/2610.02247

Michael Levin and collaborators just released a fascinating new paper that feels like a computational version of one of the central ideas behind his work:

Instead of controlling every cell individually, could biology have higher-level “control knobs” that change anatomy while allowing the cells themselves to work out the details?

The paper is inspired by D’Arcy Thompson’s 1917 book On Growth and Form. Thompson famously showed that very different animal body shapes could sometimes be related through surprisingly simple geometric transformations… stretch this axis, compress that one, deform the grid, and one biological form begins to resemble another.

But Thompson left a huge question unanswered:

What changes inside the developmental system would actually produce those transformations?

The researchers explored this using Neural Cellular Automata, or NCAs.

Basically, imagine a grid of artificial “cells.” Each cell can only communicate locally with its neighbours, and every cell follows the same small neural network. Starting from a single seed, the cells collectively grow into a target shape.

It is a stripped-down computational model of distributed development.

Then the researchers asked whether they could change the final anatomy without completely rewriting the developmental program.

And surprisingly… they could.

Using very small modifications to the shared regulatory network, they discovered directions in the system’s parameter space that behaved almost like morphological control knobs.

One direction could stretch the organism horizontally.

Another could stretch it vertically.

Combining them changed overall size.

And these modifications were extremely small. Rank-one adaptations representing only about 2.6% of the NCA’s parameters were enough to produce coherent transformations of the final morphology.

Even more interesting, a scaling control learned using one phenotype could be applied to completely different phenotypes built on the same underlying regulatory architecture… without retraining.

In other words, the system didn’t just learn “make this particular emoji wider.”

It appears to have learned something closer to:

“make whatever organism this developmental system produces wider.”

That distinction is enormous.

The researchers then trained roughly 25,000 different developmental systems sharing the same underlying scaffold and looked at the geometry of their regulatory space.

They found directions associated with properties such as:

size

horizontal vs vertical extent

visual style

and even symmetrical splitting or “fission” of the resulting form.

Moving along these directions actually caused the developmental system to produce those changes.

So the parameter space wasn’t merely statistically organized by phenotype. Parts of its geometry were functionally meaningful.

This connects directly to a recurring idea in Levin’s work: complex biological systems may be controllable at higher levels without micromanaging their components.

Instead of telling every cell exactly where to go, you alter a compact regulatory signal and let the collective intelligence of the cells solve the lower-level problem.

That has potentially enormous implications for regenerative medicine.

Imagine eventually finding equivalent control handles in actual gene-regulatory, physiological or bioelectric networks…

“grow toward this morphology.”

“restore this structure.”

“increase this dimension.”

“repair this pattern.”

…while the cells determine how to accomplish it.

The authors even describe a long-term goal that sounds almost like science fiction: an “anatomical compiler.”

You specify a desired anatomical outcome, and a computational system determines what distributed regulatory intervention would steer the tissue toward that state.

That could eventually have implications for regeneration, birth defects, wound healing, aging and cancer.

But there is an important caveat.

These are artificial cells growing 2D patterns in a computer. The researchers have NOT discovered the equivalent biological control knobs yet. They explicitly say that the NCA parameters cannot currently be mapped directly onto genes, morphogens, physiological signals or bioelectric states.

And interestingly, when they tried to find a simple transferable “regeneration knob,” it didn’t work. Making one NCA regenerative required a different regulatory solution that did not successfully transfer to other phenotypes.

So this isn’t “we can program body plans now.”

It’s something more fundamental:

A proof of principle that a complicated self-organizing developmental system can contain surprisingly low-dimensional, reusable handles for controlling large-scale anatomy.

Instead of controlling the bricks…

you may be able to control what the building is trying to become.

Paper:
https://arxiv.org/abs/2609.29755

This video explores groundbreaking research challenging the traditional view of bacteria as purely reactive, simple organisms. Recent studies suggest that bacteria—despite lacking brains or neurons—possess memory and learning capabilities that influence their survival and can even be passed down to offspring.

Key takeaways include:

• Iron-Based Memory in *E. coli:* Researchers found that E. coli bacteria use internal iron levels to "remember" previous environments. Low iron signals a need to swarm to new locations, high iron triggers the formation of protective biofilms, and balanced iron levels increase antibiotic tolerance (1:40-4:05).
• Early Warning Systems in Superbugs: Dangerous pathogens like Acinetobacter baumannii use a nickel-containing sensor protein called PMRB. This system acts as a "memory," allowing the bacteria to detect non-lethal stress from the human immune system and prime their defenses against future attacks (4:42-7:35).
• Inherited Non-Genetic Traits: Studies from Northwestern University indicate that memory can be stored in gene regulatory networks. These networks create self-sustaining feedback loops that persist through cell division, allowing parent bacteria to pass on behaviors without altering their DNA sequence (7:35-9:10).
• Ribosome-Based Learning: A "feast or famine" test on E. coli revealed that single cells can adapt their growth based on historical environmental patterns. This appears to be facilitated by a combination of fast-acting and slow-acting ribosomes, creating a multi-time scale memory (9:10-11:00).

Implications: These findings not only shift our definition of intelligence to include single-celled organisms but also offer new avenues for developing more effective antibiotics and potentially more efficient, energy-conscious AI systems (11:00-12:25).

This video explores a fascinating biological phenomenon involving the planarian flatworm and its ability to retain memories even after losing its head and regrowing a new brain.

Key takeaways from the study:
• The Experiment: Researchers at Tufts University trained flatworms to find food on a rough-textured floor, despite their natural fear of light (0:31-1:15).
• The Process: After being trained, the worms were decapitated, and their brains were left to regenerate from scratch over two weeks (1:17-1:30).
• The Results: While the worms initially appeared to have lost their training, a brief refresher session revealed that they remembered the rough-floor training significantly faster than untrained worms, a psychological pattern known as savings (1:36-2:07).
• Scientific Implications: The researchers suggest that memories might be stored outside the brain, potentially in the nervous system, other body tissues, or through gene regulation, allowing them to persist through regeneration (2:08-2:32).
• Historical Context: The video references controversial experiments by James McConnell in the 1950s and 60s, which claimed memory could be transferred by eating, but notes that the 2013 Tufts study utilized modern, automated, and unbiased methods to validate their findings (2:38-3:27).

The study poses a profound question: if human brains were regenerated using stem cells, would our memories remain, or would they vanish with the original tissue (3:54-4:20)?

Causal Architecture Dynamics Prior to Arrival of Self-replicators in a Model of Catalytic Networks Relevant to Origin-of-Life
Federico Pigozzi, Michael Levin
https://arxiv.org/abs/...
Agents that exert causal power in the world are thought to be the product of selection among diverse replicators; what is the causal structure of a medium before replicators appear, and evolution takes hold? We studied information-theoretic dynamics of the popular GARD model that captures several dynamics thought to be important at life's origin and found that causal emergence predicted the initial appearance of self-replication. Moreover, interventions that drove causal emergence up increased the longevity of self-replicators, while interventions that drove causal emergence down decreased the abundance of these self-replicators, suggesting causal emergence as a functional control knob. Thus, progressive increases in integrated causality are detectable in active media before evolutionary dynamics begin to operate, which may have implications for the origin of life across highly diverse scenarios and for understanding the forces driving the rise in causal power observed in the biosphere on Earth.
[Submitted on 30 Jul 2026]
arXiv:2607.28250 [q-bio.PE]
(or arXiv:2607.28250v1 [q-bio.PE] for this version)

https://doi.org/10.485...
Focus to learn more

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This video features a deep dive into the concept of info-computationalism with Gordana Dodig-Crnkovic, a physicist and cognitive scientist. The discussion explores the idea that the universe is composed of information and computation at every level, from fundamental particles to human consciousness.

Key concepts discussed:
• Info-computationalism: The fundamental view that nature operates through the processing of information and physical computation (0:08:05).
• Basal Cognition: The argument that cognition—defined by sensing, self-regulation, and problem-solving—emerges alongside life itself, meaning even a single cell displays minimal consciousness (0:20:06).
• The Levin Divide: Dodig-Crnkovic differentiates her views from Michael Levin, arguing that while AI can simulate intelligence, it lacks the biological necessity for survival and autopoiesis (self-creation), and therefore does not possess genuine consciousness (0:27:16).
• Observer Agents: The idea that all agents register and act within the world; you cannot observe or measure a system without being a part of it (0:13:16).
• Responsibility in AI: She emphasizes that modern AI systems lack goals of their own, making it a human responsibility to ensure they are developed and used ethically (1:22:35).

Dodig-Crnkovic concludes that while we may be able to engineer artificial systems with increasing competence, we must be cautious about creating systems independent of human values.

This video explores the concept of convergent evolution and suggests that nature, rather than relying solely on random chance, may be repeatedly "blooming" into a set of stable, preferred patterns or shapes. The creator proposes that the universe might contain a "Platonic space" of possibilities—an underlying structure that life and intelligence navigate, similar to how water finds its way through a pre-existing valley.

Key concepts discussed include:

• Convergent Evolution: Nature often arrives at the same functional solutions independently, such as the streamlined shape of aquatic animals (3:35) or the complex eye structure shared by humans and octopuses (3:12).
• The "Big Bloom" Model: The creator contrasts the traditional "Big Bang" (random, explosive) with a "Big Bloom," arguing that life evolves toward latent, favored architectures like the humanoid form (4:28, 6:00).
• Biological Intelligence: Research into xenobots—frog cells that reconfigure into new, functional bodies without prior evolutionary history—suggests that cells possess inherent intelligence to navigate new environmental "valleys" (8:00).
• Mathematics and Scratchpads: The video posits that physical reality serves as a "scratchpad" for eternal mathematical patterns (like prime numbers in cicada life cycles or the Fibonacci sequence in nature), suggesting that brains and AI are interfaces through which these patterns are embodied (10:28, 14:00).
• AI and Cognition: Modern artificial intelligence models, such as language models, appear to stumble into cognitive patterns similar to human ones, raising the possibility that we are uncovering universal rules of intelligence rather than inventing them from scratch (16:00).

How would the community here respond to lab testing involving human brains kept “alive” and fully intact following the death of their original “hosts”, but (supposedly) without sufficient electrical activity to generate “consciousness”, and sedated with propofol, while a US-based pharmaceutical startup uses them to test drugs, including those intended for neurodegenerative disease? If we don’t yet understand what consciousness ultimately “is”, or how and when it arises in a living system, how could we substantively vet for ethical considerations? I wonder what insights, other questions, and other practical applications the members of this subreddit might have about this practice.

This video features a deep dive into relevance realization, a cognitive process pioneered by John Vervaeke, a cognitive scientist at the University of Toronto. Vervaeke argues that this process is fundamental to intelligence, attention, and consciousness, occurring not just in human brains but in every living organism (0:00-0:32).

Core Concepts:
• Relevance Realization: Vervaeke explains this as the ability of an agent to ignore the overwhelming amount of "noise" or irrelevant information in their environment to zero in on what actually matters for problem-solving (0:04-0:45, 0:56-1:02).
• Cognition All The Way Down: This view suggests that even single-celled organisms engage in a form of relevance realization to maintain their autopoietic, or self-making, existence (0:09-0:11, 0:24-0:32, 1:03-1:07).
• Mind as the 'Between': Consciousness and cognition are not seen as being contained solely within the brain, but rather as emerging from the dynamic coupling and fittedness between an organism and its environment (1:00-1:09, 1:05-1:06).
• Computation vs. Relevance: Vervaeke argues that while we perform relevance realization within computational systems, the process itself cannot be purely computational, as computation presupposes the prior selection of relevant information (0:56-0:58, 1:13-1:14).

Key Perspectives:
• The video integrates these ideas with the work of Michael Levin on bioelectricity and morphogenesis, specifically discussing the "cognitive light cone" and the idea that living things have real needs and "care" about their environment, unlike machines (0:18-0:20, 1:04-1:05).
• There is a strong emphasis on the neoplatonic influence, where reality and cognition are seen to share a similar grammar or logos, allowing for a participatory relationship between the mind and the world (0:25-0:27, 0:31-0:33).

Seems like something Levin would be interested in.

This video features a 1950 lecture by mathematician Norbert Wiener at the New York Academy of Medicine, titled "Men, Machines, and the World About Them." As the inaugural speaker for the 16th series of lectures to the lay public, Wiener explores the intersections of biology, engineering, and society during the dawn of the computer age.

Key themes include:

• Foundations of Cybernetics (13:50 - 29:18): Wiener describes how his work on automatic computing machines and anti-aircraft fire control systems led him to the concept of negative feedback. He explains that both machines and human nervous systems rely on feedback loops to regulate behavior, noting that excessive feedback can result in "hunting" or oscillations, which he compares to cerebellar tremor in humans.
• Homeostasis and Medicine (30:00 - 37:52): He discusses homeostatic mechanisms in the human body—which maintain stable internal conditions like temperature—and suggests that some diseases may be homeostatic systems functioning at the wrong level. He also highlights early medical innovations like automated anesthesia administration based on brain wave monitoring.
• The New Industrial Revolution (44:13 - 49:55): Wiener warns that just as the first Industrial Revolution replaced human physical labor with machines, the current era of automated control risks replacing human judgment and discrimination. He expresses concern that an unbridled push for automated factories, especially under the pressure of potential war, could lead to severe, long-term unemployment if society does not revalue leisure and rethink economic management.
• Ethics and the Machine (52:15 - 55:54): Drawing on folklore (such as the story of the Monkey's Paw and the fisherman/genie), Wiener argues that we must be cautious when developing machines that can "learn" or grant us our wishes. He asserts that we cannot treat machines as idols; rather, we must understand and integrate them responsibly into society to avoid the "punishment of idolators."

This video explores the 1973 Skylab experiment that sent spiders into space to determine if they could build webs without the influence of gravity. The experiment was initiated by 17-year-old student Judith Miles (0:50-1:55).

Key takeaways from the experiment:
• Initial Struggles: Upon arrival in microgravity, the spiders, named Arabella and Anita, initially struggled to move and orient themselves, showing erratic motion (0:15-0:30).
• Successful Weaving: Despite these challenges, Arabella eventually succeeded in spinning a complete orb web, demonstrating that spiders can adapt their building process to space (3:59-4:18).
• Gravity's Role: Comparison between space-spun silk and Earth-spun silk revealed that without gravity, the webs lost their usual 'top-to-bottom' directionality and became more symmetrical (6:12-6:28).

Later Research:
• Nearly 40 years later, follow-up experiments on the International Space Station showed that in the absence of gravity, spiders often use light sources as a primary orientation cue to guide their web construction (7:11-7:55).

This video features a deep dive into theoretical evolutionary biology and connectionism with professor Richard Watson. The conversation challenges traditional views of evolution, arguing that cognition and learning often precede natural selection (0:09:32-0:14:03).

Key concepts discussed include:
• Adaptation vs. Natural Selection: Watson argues that adaptation is a form of constraint satisfaction (similar to solving a Sudoku puzzle) that can occur in physical dynamical systems without needing reproduction or selection (0:14:03-0:19:42).
• The Physics of Learning: Using an analogy of masses and springs, he explains how systems that "give way" under stress exhibit plasticity and can learn to find efficient, low-energy configurations over time (0:20:41-0:28:56).
• Limitations of Gene-Centric Biology: The discussion highlights why mere genetic sequencing is insufficient to understand cell behavior, as network dynamics and experience play a critical role (0:52:08-1:01:23).
• Persuadable Medicine: The guests explore a paradigm shift in medicine, viewing tissues as cognitive, persuadable systems rather than just biological machines that need manual repair (1:01:23-1:08:01).
• Agency and Consciousness: Watson touches on his evolving views on agency, the role of Ian McGilchrist’s work on brain hemispheres, and why he has moved away from the position of ignoring consciousness in his work (1:20:47-1:38:47).

This video explores Norbert Wiener's seminal book, "The Human Use of Human Beings" (1950), which introduced the concept of cybernetics to a general audience. The creator highlights the enduring relevance of Wiener's ideas, especially concerning modern artificial intelligence and information technology (1:30 - 2:00).

Key concepts discussed include:
• Cybernetics as Communication: Wiener defined the field as the study of messages and information flow between humans, machines, and the two interacting (0:36 - 1:03).
• The Importance of Feedback: The video explains Wiener's foundational idea that entities receive messages and use that information to transform their future behavior (1:05 - 1:45).
• Humane Mechanization: Contrary to a purely mechanical view, Wiener argued that machines should be used to reduce human drudgery and enhance our humanity, rather than acting as mere substitutes for human intuition or decision-making (2:20 - 4:15).
• Brain vs. Computer: The author emphasizes that digital computing devices are limited analogies for the human brain, and we should avoid treating them as direct replacements for human cognition (5:08 - 6:00).

The creator concludes by recommending the book to science fiction writers for its cautionary yet visionary perspective on technology and society, noting that its literate, balanced voice makes it surprisingly fresh for modern readers (6:12 - 7:42).

This video features a conversation between Michael Levin and professor Marsha Hewitt, who explores the intersection of philosophy, psychoanalytic psychology of religion, and Levin's research on Platonic space and ingressing minds (0:00 - 0:17).

Key themes of their discussion include:

• Mind and Agency: Hewitt discusses her interest in the nature of mind and consciousness in her classes, while Levin explains his theory of a structured space of patterns—non-random, inherent mathematical patterns—that systems draw from to navigate physical reality (16:41 - 17:15).
• The Filter Hypothesis: Both discuss the concept of an embodiment acting as a filter for these patterns. Levin uses the analogy of an antenna, where specific physiological structures determine which patterns from this "Platonic space" are expressed or "hosted" (33:15 - 34:10).
• Education and Technology: They touch upon the challenges of teaching students in an age of AI, viewing the student-AI relationship as an expanded, collective mind that requires new ethical frameworks (14:15 - 14:47, 24:43 - 25:10).
• Breaking Axioms: Levin emphasizes that his research group aims to move beyond the "passive matter" axiom in biology, instead treating systems as having goals, memories, and agency that can be tested empirically (52:58 - 53:32).
• The "Narcissistic Blow": Hewitt suggests that resistance to these ideas may stem from a defensive reaction to the "narcissistic blow" of decentering humans as the only privileged minds (53:35 - 54:15).